NDLI: Image annotation by $\textit{k}NN-sparse$ graph-based label propagation over noisily tagged web images

Please wait, while we are loading the content...

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 8

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 7

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 6

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 5

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 4

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 3

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 2

Issue 4, July 2011

Issue 3, April 2011

Issue 2, February 2011

Introduction to the special issue on intelligent multimedia systems and technology

Active learning in multimedia annotation and retrieval: A survey

Variational inference with graph regularization for image annotation

Collection-based sparse label propagation and its application on social group suggestion from photos

Distance metric learning from uncertain side information for automated photo tagging

Image annotation by kNN-sparse graph-based label propagation over noisily tagged web images

Automatic player labeling, tracking and field registration and trajectory mapping in broadcast soccer video

Neighboring joint density-based JPEG steganalysis

Probabilistic temporal multimedia data mining

Issue 1, January 2011

ACM Transactions on Intelligent Systems and Technology (TIST) : Volume 1

Image annotation by $\textit{k}NN-sparse$ graph-based label propagation over noisily tagged web images

Content Provider	ACM Digital Library
Author	Hong, Richang Yan, Shuicheng Tang, Jinhui Jain, Ramesh Chua, Tat-Seng Qi, Guo-Jun
Copyright Year	2011
Abstract	In this article, we exploit the problem of annotating a large-scale image corpus by label propagation over noisily tagged web images. To annotate the images more accurately, we propose a novel $\textit{k}NN-sparse$ graph-based semi-supervised learning approach for harnessing the labeled and unlabeled data simultaneously. The sparse graph constructed by datum-wise $one-vs-\textit{k}NN$ sparse reconstructions of all samples can remove most of the semantically unrelated links among the data, and thus it is more robust and discriminative than the conventional graphs. Meanwhile, we apply the approximate $\textit{k}$ nearest neighbors to accelerate the sparse graph construction without loosing its effectiveness. More importantly, we propose an effective training label refinement strategy within this graph-based learning framework to handle the noise in the training labels, by bringing in a dual regularization for both the quantity and sparsity of the noise. We conduct extensive experiments on a real-world image database consisting of 55,615 Flickr images and noisily tagged training labels. The results demonstrate both the effectiveness and efficiency of the proposed approach and its capability to deal with the noise in the training labels.
Starting Page	1
Ending Page	15
Page Count	15
File Format	PDF
ISSN	21576904
e-ISSN	21576912
DOI	10.1145/1899412.1899418
Volume Number	2
Issue Number	2
Journal	ACM Transactions on Intelligent Systems and Technology (TIST)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-02-24
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	$\textit{k}NN$ Sparse graph Label propagation Noisy tags Semi-supervised learning Web image
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Theoretical Computer Science

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Inferring semantic concepts from community-contributed images and noisy tags

Label propagation based semi-supervised learning for software defect prediction

SELP: Semi-supervised evidential label propagation algorithm for graph data clustering

Using Sparse Patch Annotation for Tumor Segmentation in Histopathological Images

Semi-supervised dictionary learning with label propagation for image classification

Multi-View Graph Fusion for Semi-Supervised Learning: Application to Image-Based Face Beauty Prediction

Variational inference with graph regularization for image annotation

Graph-Based Label Propagation in Digital Media: A Review

Theme-Aware Semi-Supervised Image Aesthetic Quality Assessment

Image annotation by $\textit{k}NN-sparse$ graph-based label propagation over noisily tagged web images

Similar Documents

Inferring semantic concepts from community-contributed images and noisy tags

Label propagation based semi-supervised learning for software defect prediction

SELP: Semi-supervised evidential label propagation algorithm for graph data clustering

Using Sparse Patch Annotation for Tumor Segmentation in Histopathological Images

Semi-supervised dictionary learning with label propagation for image classification

Multi-View Graph Fusion for Semi-Supervised Learning: Application to Image-Based Face Beauty Prediction

Variational inference with graph regularization for image annotation

Graph-Based Label Propagation in Digital Media: A Review

Theme-Aware Semi-Supervised Image Aesthetic Quality Assessment

Image annotation by $\textit{k}NN-sparse$ graph-based label propagation over noisily tagged web images